TY - GEN
T1 - Fetching Relevant Images and Videos in Keyword Based Search Mechanism with Hypergraph Learning and Similarity Matching
AU - Patil, Sheetal D.
AU - Kshirsagar, Vivek
AU - Nagori, Meghana
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/16
Y1 - 2018/10/16
N2 - This paper is an attempt for the work done for image and video retrieval system. Content-Based Image Retrieval(CBIR) and Content-Based Video Retrieval(CBVR), has attracted researchers from various research fields like, computer vision, artificial intelligence, human factors, machine learning, image processing, man-machine modeling to name a few. Existing methods have revealed certain flaws like noisy data, often leading to display of irrelevant images or videos. In our system we have used Hypergraph Learning for images and Similarity Matching for videos. All the above challenges are addressed by retrieving relevant images and videos in response to user's keyword based search approach. User can search by attributes present during search or user can search by new attribute which gets added in the attribute list in the database and we can have ranking of the retrieved results and get relevant data. Experimentation is carried out from Flickr database for images and videos under consideration are those which are available on YouTube. A database of images and videos that cover user's interest in diverse domains is designed and used in experimentation.
AB - This paper is an attempt for the work done for image and video retrieval system. Content-Based Image Retrieval(CBIR) and Content-Based Video Retrieval(CBVR), has attracted researchers from various research fields like, computer vision, artificial intelligence, human factors, machine learning, image processing, man-machine modeling to name a few. Existing methods have revealed certain flaws like noisy data, often leading to display of irrelevant images or videos. In our system we have used Hypergraph Learning for images and Similarity Matching for videos. All the above challenges are addressed by retrieving relevant images and videos in response to user's keyword based search approach. User can search by attributes present during search or user can search by new attribute which gets added in the attribute list in the database and we can have ranking of the retrieved results and get relevant data. Experimentation is carried out from Flickr database for images and videos under consideration are those which are available on YouTube. A database of images and videos that cover user's interest in diverse domains is designed and used in experimentation.
KW - Hypergraph learning
KW - Image and video search
KW - re-ranking
KW - semantic attributes
KW - Similarity matching
UR - http://www.scopus.com/inward/record.url?scp=85056807737&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT.2018.8494191
DO - 10.1109/ICCCNT.2018.8494191
M3 - Conference contribution
AN - SCOPUS:85056807737
T3 - 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018
BT - 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018
Y2 - 10 July 2018 through 12 July 2018
ER -